A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size

Abstract

<p style='text-indent:20px;'>The existing method of determining the size of the time series sliding window by empirical value exists some problems which should be solved urgently, such as when considering a large amount of information and high density of the original measurement data collected from industry equipment, the important information of the data cannot be maximally retained, and the calculation complexity is high. Therefore, by studying the effect of sliding window on time series similarity technology in practical application, an algorithm to determine the initial size of the sliding window is proposed. The upper and lower boundary curves with a higher fitting degree are constructed, and the trend weighting is introduced into the <inline-formula><tex-math id="M1">\begin{document}$ LB\_Hust $\end{document}</tex-math></inline-formula> distance calculation method to reduce the difficulty of mathematical modeling and improve the efficiency of data similarity computation.